Ford Global Technolgoies, LLC

Argo Ai Gmbh

Deva K Ramanan

Andrew Thomas Hartnett

Guy Hotson

Richard L Kwant

Jagjeet Singh

Nicolas Cebron

William Junbo Qi

Siddhesh Shyam Khandelwal

Brett Browning

Chao Fang

Neehar Peri

Mengtian Li

Chitteshwaran Thavamani

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes detecting an actor that may move within a scene in the environment by an object detection system of a vehicle in the environment, determining a kinematic history of the actor, and using context of the scene and the kinematic history of the actor to determine a plurality of reference polylines for the actor. The method further includes generating a contextual embedding of the kinematic history of the actor to generate a plurality of predicted trajectories of the actor, in which the generating conditions each of the predicted trajectories to correspond to one of the reference polylines. The method additionally includes using, by the vehicle, the plurality of predicted trajectories to plan movement of the vehicle.

Path-conditioned motion forecasting for vehicle motion planning

Systems and methods for processing high resolution images are disclosed. The methods include generating a saliency map of a received high-resolution image using a saliency model. The saliency map includes a saliency value associated with each of a plurality of pixels of the high-resolution image. The method then includes using the saliency map for generating an inverse transformation function that is representative of an inverse mapping of one or more first pixel coordinates in a warped image to one or more second pixel coordinates in the high-resolution image, and implementing an image warp for converting the high-resolution image to the warped image using the inverse transformation function. The warped image is a foveated image that includes at least one region having a higher resolution than one or more other regions of the warped image.

Systems and methods for generating object detection labels using foveated image magnification for autonomous driving

Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to detect objects and forecast their predicted positions. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take at a future time can be generated, the plurality of trajectories being based on a generated three-dimensional (3D) point cloud map indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated along one or more of the plurality of trajectories. A maneuver for the AV can be performed based on the forecasted position.

End-to-end systems and methods for streaming 3D detection and forecasting from lidar point clouds

Methods and systems for detecting objects near an autonomous vehicle (AV) are disclosed. An AV will capture an image. A trained network will process the image at a lower resolution and generate a first feature map that classifies object(s) within the image. The system will crop the image and use the network to process the cropped section at a higher resolution to generate a second feature map that classifies object(s) that appear within the cropped section. The system will crop the first feature map to match a corresponding region of the cropped section of the image. The system will fuse the cropped first and second feature maps to generate a third feature map. The system may output the object classifications in the third feature map to an AV system, such as a motion planning system that will use the object classifications to plan a trajectory for the AV.

Attentional sampling for long range detection in autonomous vehicles

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes, by an object detection system of a vehicle in an environment, detecting an actor that may move within a scene in the environment. The method further includes using context of the scene to determine a reference polyline for the actor and determining a kinematic history of the actor. The method additionally includes using the kinematic history to predict a waypoint, which is a predicted position of the actor at a conclusion of a waypoint time period, and identifying a segment of the reference polyline, the segment extending from a current location to a point along the reference polyline that is closest to the waypoint and determining a trajectory for the actor conditioned by the segment of the reference polyline.

Waypoint prediction for vehicle motion planning

A method is disclosed for evaluating a classifier used to determine a traffic light signal state in images. The method includes, by a computer vision system of a vehicle, receiving at least one image of a traffic signal device of an imminent intersection. The traffic signal device includes a traffic signal face including one or more traffic signal elements. The method includes classifying, by a traffic light classifier (TLC), a classification state of the traffic signal face using labeled images correlated to the received at least one image. The classification state controls an operation of the vehicle at the intersection. The method includes evaluating a performance of the classifying of the classification state generated by the TLC. The evaluation is a label-free performance evaluation based on unlabeled images. The method includes training the TLC based on the evaluated performance.

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Pittsburgh, PA, United States of America

Deva K Ramanan